Transfer Learning is a method which helps us in building an accurate model without starting the process from scratch. It saves us a lot of time and is useful because we use pre-trained models which does not need a large dataset. In this paper we have used VGG16 & VGG19[1]}, Resnet50[2]}, InceptionV3[3]}, Xception[4]} models. The input size for all the models is (224 \(\times \)  224 \(\times \)  3). The problem that we usually face in using a pre-trained model is overfitting. In our case we used data Augmentation and Dropout to address this issue. After the convolutional base the flatten layer is placed which transforms the 2-D feature matrix into a vector. After that fully connected layer with Rectified Linear Unit(ReLU) is used. The output is then fed to Softmax activation Layer for final classification. Fig. REF  shows how a model is constructed using transfer learning.
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